title: Automatic classification of hyperactive children: comparing multiple artificial intelligence approaches.
abstract: Automatic classification of different behavioral disorders with many similarities (e.g. in symptoms) by using an automated approach will help psychiatrists to concentrate on correct disorder and its treatment as soon as possible, to avoid wasting time on diagnosis, and to increase the accuracy of diagnosis. In this study, we tried to differentiate and classify (diagnose) 306 children with many similar symptoms and different behavioral disorders such as ADHD, depression, anxiety, comorbid depression and anxiety and conduct disorder with high accuracy. Classification was based on the symptoms and their severity. With examining 16 different available classifiers, by using "Prtools", we have proposed nearest mean classifier as the most accurate classifier with 96.92% accuracy in this research.
authors.affiliation: Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
authors.forename: Mona
authors.initials: M
authors.lastname: Delavarian
authors.forename: Farzad
authors.initials: F
authors.lastname: Towhidkhah
authors.forename: Shahriar
authors.initials: S
authors.lastname: Gharibzadeh
authors.forename: Parvin
authors.initials: P
authors.lastname: Dibajnia
mesh_terms: D001185:Artificial Intelligence; D002648:Child; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D006948:Hyperkinesis / Q000150:complications / Q000175:diagnosis*; D008297:Male; D001523:Mental Disorders / Q000150:complications / Q000175:diagnosis*; D011569:Psychiatric Status Rating Scales; D012720:Severity of Illness Index